If the nonzero components of
the sparse vectors are grouped together then the outer product results in
a matrix with a nonzero block and zeros elsewhere.

The model selection is performed by a constraint optimization
according
to Hoyer, 2004. The Euclidean distance (the Frobenius norm) is
minimized subject to sparseness and non-negativity constraints.

Model selection is done by gradient descent on the Euclidean
objective and thereafter projection of single vectors of
L and single vectors of Z
to fulfill the sparseness and non-negativity constraints.

The projection minimize the Euclidean distance
to the original vector given an
l_1-norm and an l_2-norm and enforcing non-negativity.

The projection is a convex quadratic problem which is solved
iteratively where at each iteration at least one component is set to
zero. Instead of the l_1-norm a sparseness measurement is used
which relates the l_1-norm to the l_2-norm.